Coronavirus Disease 2019 (COVID-19) Pneumonia Presentations in Chest Computed Tomography: A Pictorial Review

Hochhegger, Bruno; Mandelli, Nicole S.; Stüker, Guilherme; Meirelles, Gustavo de Souza Portes; Zanon, Matheus; Mohammed, Tan-Lucien; Verma, Nupur; Sverzellati, Nicola; Marchiori, Edson

doi:10.1067/j.cpradiol.2020.06.010

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Chest CT and its scoring is very useful in determining pulmonary involvement of COVID-19 ( 4 – 11 ). In our single-centre study, 233 COVID-19 patients were admitted to hospital ( 23 ) and 227 patients had chest CT scans.…”

Section: Discussionmentioning

confidence: 99%

“…We reviewed all clinical electronic medical records, laboratory reports, as well as chest CT and X-ray images and collected a set of data as described previously ( 23 ). The CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, as well as A-DROP scores ( 11 ). Chest CT assessment was performed by three qualified radiologists (LK, PT, OCS).…”

Section: Methodsmentioning

confidence: 99%

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The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

et al. 2023

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IntroductionChest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19.Patients and methodsAltogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems.ResultsThe mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance.ConclusionCTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

et al. 2023

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“…On chest CT examinations of patients with COVID-19, the main findings are ground-glass opacities, crazy-paving pattern, consolidations, reticular opacities, subpleural lines, inverted halo sign, and pleural thickening (in 32% of cases). Less common findings include changes in the airways, vascular dilation, pulmonary nodules, lymph node enlargement (in 4-8% of cases), pleural effusion (in 5-15%), and pericardial effusion (in 5%), the latter three usually indicating a worse prognosis, either due to decompensation of pre-existing heart disease or the development of acute heart failure, arrhythmias, or acute cardiovascular injury ( 19 - 22 ) .…”

Section: Imaging Aspects Of Covid-19mentioning

confidence: 99%

COVID-19: a brief update for radiologists

Meirelles

2020

Radiol Bras

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Coronavirus disease 2019 (COVID-19), which is caused by a new coronavirus-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-is a pandemic with major impacts on the health care sector, and a broad view of the disease is of fundamental importance for any radiologist. The purpose of this review is to address the main clinical and imaging aspects of COVID-19, as well as guidelines for requesting and using imaging methods; measures to protect patients and health care professionals; systems for quantifying pulmonary findings and preparing integrated reports; and the main innovations that have emerged during this pandemic.

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“…In general, CXR imagery and CT imagery are able to clearly illustrate the difference between healthy lung conditions and those infected by disease clearly. However in some cases CT imagery results show abnormal findings [19] and are not recommended for use in the diagnosis of patients without symptoms [20]. This is in line with most studies focusing on the classification between images of healthy lungs and those infected by Covid-19, whereas the symptoms caused by Covid-19 are very similar to pneumonia symptoms in particular when viewed based on the visual features of imagery [21]- [24] The appearance of a healthy lung image will look very different from the lungs infected by Covid-19 so that it can be distinguished easily by the human eye.…”

Section: Introductionmentioning

confidence: 99%

New Image Texture Feature for Chest X-Ray Classification

Adi

Nugroho

Astuti

2022

JAIS

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This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.

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Coronavirus Disease 2019 (COVID-19) Pneumonia Presentations in Chest Computed Tomography: A Pictorial Review

Cited by 5 publications

References 12 publications

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

COVID-19: a brief update for radiologists

New Image Texture Feature for Chest X-Ray Classification

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